1. Field of the Invention
The present invention relates to a technique for inspecting pattern on an object.
2. Description of the Background Art
A comparison check method has been mainly used, conventionally, in a field of inspection of pattern formed on a semiconductor substrate, a glass substrate, a printed circuit board or the like. For example, in binary image, an image of exclusive OR of an inspection image (an image to be inspected) and a reference image is obtained, and in grayscale image, an absolute difference value image which represents absolute values of the difference between pixel values in an inspection image and pixel values in a reference image is obtained and each pixel is binarized by a predetermined threshold value, to detect an area of a defect.
In the circuit formation process or the like of a semiconductor substrate, a pattern inspection apparatus with a function to classify a class of a detected defect automatically (i.e., to classify a defect automatically) is used to specify the cause of a low yield and improve manufacturing conditions in each operation. In general, classification of a defect is performed in accordance with classification conditions given by a user in advance (on a so-called rule base) or by using a method based on learning such as discriminant analysis or neural network on the basis of a feature value obtained from an area in an inspection image or a reference image corresponding to a defect.
Japanese Examined Patent Application Laid Open Gazette No. 5-13256 (Document 1) discloses a method where a binary image which represents defects is divided into partial areas by using a reference image divided into areas and a feature value of the defect is obtained in each partial area, to classify a class of the defect on a rule base. Japanese Patent Application Laid Open Gazette No. 9-186208 (Document 2) suggests a method where a grayscale inspection image is separated into a wire portion and a background portion, and a feature value representing density of defect is acquired in each portion, to classify the defect on the basis of the feature value. Japanese Patent Application Laid Open Gazette No. 8-21803 (Document 3) discloses a method where geometric feature values such as an area (i.e., the number of pixels), length of circumference, feret diameter, roundness, centroid position are mainly obtained and inputted to neural network, to perform a high-level classification of a defect.
Japanese Patent Application Laid Open Gazette No. 2001-99625 (Document 4) suggests a method where average density, an autocorrelation feature value or the like of an area in an inspection image or a reference image corresponding to a specified defect candidate is obtained, to classify whether the defect candidate is true or false on a rule base. Japanese Patent Application Laid Open Gazette No. 2002-22421 (Document 5) discloses a technique where two differential images between an inspection image and two reference images are generated, values of pixels in the two differential images are converted into error probability values by using a standard deviation of the pixel values to generate two error probability value images. Further, products of the values of corresponding pixels in the two error probability value images are obtained to generate a probability product image, and value of each pixel in the probability product image is compared with a predetermined threshold value to determine whether there is a defect or not on an object.
Recently, it is required to increase the accuracy of classification of defects, however, even if geometric feature values or feature values representing density are obtained as the methods disclosed in Documents 1 or 3, it is difficult to classify classes of defects with high accuracy in some cases. In this case, even if the method based on leaning such as neural network in Document 3 is used, since the accuracy of classification largely depends on a type of inputted feature value in general, it is not possible to meet the required accuracy.
On the contrary, by applying the method of Document 4 obtaining an autocorrelation feature value to classification of defects, it is expected to classify with certain accuracy, but depending on an object to be inspected, it is needed to increase the accuracy of classification. To classify by using an autocorrelation feature value on a rule base, normally, it needs to determine classification conditions by complicated operation. Thus, it is not possible to perform a classification of defects easily.